Next Article in Journal
A Bayesian Scene-Prior-Based Deep Network Model for Face Verification
Next Article in Special Issue
An Intelligent System for Monitoring Skin Diseases
Previous Article in Journal
Energy-Aware RFID Anti-Collision Protocol
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(6), 1905; https://doi.org/10.3390/s18061905

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

1
Department of Smart Systems Technologies, Alpen-Adira University, Klagenfurt 9020, Austria
2
Research Center Borstel—Leibniz Center for Medicine and Biosciences, Borstel 23845, Germany
3
Carl Gustav Carus Faculty of Medicine, Dresden University of Technology, Dresden 01069, Germany
*
Author to whom correspondence should be addressed.
Received: 13 May 2018 / Revised: 4 June 2018 / Accepted: 7 June 2018 / Published: 11 June 2018
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
Full-Text   |   PDF [447 KB, uploaded 12 June 2018]   |  

Abstract

Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions. In this paper, we explore a novel robust subject-independent human emotion recognition system, which consists of two major models. The first one is an automatic feature calibration model and the second one is a classification model based on Cellular Neural Networks (CNN). The proposed system produces state-of-the-art results with an accuracy rate between 80% and 89% when using the same elicitation materials and physiological sensors brands for both training and testing and an accuracy rate of 71.05% when the elicitation materials and physiological sensors brands used in training are different from those used in training. Here, the following physiological signals are involved: ECG (Electrocardiogram), EDA (Electrodermal activity) and ST (Skin-Temperature). View Full-Text
Keywords: emotion recognition; classification; dynamic calibration; cellular neural networks (CNN); physiological signals emotion recognition; classification; dynamic calibration; cellular neural networks (CNN); physiological signals
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ali, M.; Al Machot, F.; Haj Mosa, A.; Jdeed, M.; Al Machot, E.; Kyamakya, K. A Globally Generalized Emotion Recognition System Involving Different Physiological Signals. Sensors 2018, 18, 1905.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top